Forecasting or prediction is a very important activity in economics, commerce, and various branches of science. Forecasting is the process of predicting the outcomes of events that have not yet occurred. Forecasting can be done by various methods. One such method uses regression analysis. Regression analysis is a statistical analysis technique, which can be used to model a real valued target variable as a function of one or more real valued input attributes.
In several forecasting or prediction applications, it is seen that prediction errors in one direction are more tolerable than the other. For instance, while processing a credit card suitability or loan suitability screening application, a bank might feel that false positives are extremely dangerous, while false negatives are tolerable. That is, giving a credit card or a loan to a person who does not qualify for it, is relatively more dangerous than refusing a credit card or a loan to a person who might have marginally qualified for it. That is, over predicting the suitability score in a loan application is relatively more dangerous as compared to under predicting it.
U.S. Pat. No. 7,349,823 B2 describes a method for optimizing the regression model used for prediction of a signal as a function of a set of available signals using more than one regression model. This method includes receiving training data sets from the set of available signals. Further, it includes initializing a set of regression models, which includes at least two regression models. In addition, the method includes creating a genetic offspring of the set of regression models. However, this method does not reduce the error present in the set of available signals. As a result, the deviation in the forecasted values is high.
U.S. Pat. No. 7,702,053 B2 describes a device for error calculation. The device includes an error calculation circuit configured to determine the error present in a signal. Further, it includes a processing circuit configured to adjust the signal in accordance with the coefficients of the processing circuit. In addition, it includes a dithering circuit configured to adjust the coefficients of the processing circuit. However, this device uses only one model to reduce the error. As a result, the error minimized is to a lesser degree.
In light of the above discussion, there is a need for a method and a system to minimize prediction errors in a preferred direction, thereby penalizing one of over prediction or under prediction higher than the other.